Universal computation by networks of model cortical columns

We present a model cortical column consisting of recurrently connected, continuous-time sigmoid activation units that provides a building block for neural models of complex cognition. Recent progress with a hybrid neural/symbolic cognitive model of problem-solving [T. A. Polk et. al., 2002] prompted us to investigate the adequacy of these columns for the construction of purely neural cognitive models. Here we examine the computational power of networks of columns and show that every Turing machine maps in a straightforward fashion onto such a network. Furthermore, several hierarchical structures composed of columns that are critical in this mapping promise to provide biologically plausible models of timing circuits, gating mechanisms, activation-based short-term memory, and simple if-then rules that will likely be necessary in neural models of higher cognition.